PREDICTIVE MODELING OF INACTIVATION, INJURY, AND RECOVERY OF LISTERIA MONOCYTOGENES UNDER COMBINED EFFECT OF HIGH PRESSURE AND TEMPERATURE

Save this PDF as:
 WORD  PNG  TXT  JPG

Size: px
Start display at page:

Download "PREDICTIVE MODELING OF INACTIVATION, INJURY, AND RECOVERY OF LISTERIA MONOCYTOGENES UNDER COMBINED EFFECT OF HIGH PRESSURE AND TEMPERATURE"

Transcription

1 The Pennsylvania State University The Graduate School College of Engineering PREDICTIVE MODELING OF INACTIVATION, INJURY, AND RECOVERY OF LISTERIA MONOCYTOGENES UNDER COMBINED EFFECT OF HIGH PRESSURE AND TEMPERATURE A Dissertation in Agricultural and Biological Engineering by Niharika Mishra 211 Niharika Mishra Submitted in Partial Fulfillment of the Requirements for the Degree of Doctor of Philosophy May 211

2 The dissertation of Niharika Mishra was reviewed and approved* by the following: Virendra M. Puri Graduate Officer for the Department of Agricultural and Biological Engineering Distinguished Professor of Agricultural and Biological Engineering Dissertation Adviser Chair of Committee Ali Demirci Professor of Agricultural and Biological Engineering Ramaswamy C. Anantheswaran Professor of Food Science Stephen J. Knabel Professor of Food Science *Signatures are on file in the Graduate School. ii

3 Abstract Milk and dairy products are a rich source of macronutrients, proteins, fats and carbohydrates; they are a very important source of calcium and riboflavin and provide significant amounts of most other essential nutrients. The U.S. and many other countries are supplied with billions of pounds of dairy products each year by 65, working dairy farms in the U.S. Therefore, the dairy industry plays a vital role in the U.S. economy. The annual milk production in the year 2 was billion pounds which is worth more than $2 billion (IDFA, 21). During processing of milk and dairy products, often times, microbial contaminations occur due to improper cleaning and handling of the processing equipment leading to several outbreaks. Among the several pathogenic microorganisms Listeria monocytogenes is one which is a potential threat to milk and dairy product because of its ability to grow at refrigeration temperatures. High pressure processing (HPP) is gaining popularity among the nonthermal food processing technologies as it can maintain the freshness of the food products after the treatment. HPP can inactivate microorganisms in the pressure range of 5-7 MPa, extending the shelf-life of the treated food material. L. monocytogenes shows resistance to pressure when present in milk medium. Therefore, it is important to study and characterize the resistance of L. monocytogenes in milk under combined effects of high pressure and temperature. Predictive modeling is an important tool for quantitative determination of the growth and inactivation of microorganisms under specific environmental conditions. At present, only a few predictive models related to inactivation kinetics of L. monocytogenes under the combined effects of temperature and pressure are known. Moreover, very limited work has been done on injury and recovery of microorganisms after heat and pressure treatment. Therefore, the overall goal of the study was to evaluate the inactivation, injury, and recovery of L. monocytogenes and to develop an overall iii

4 predictive model for injury and recovery of L. monocytogenes under the combined effect of temperature and pressure. In the current study, three pressure levels (4, 5, and 6 MPa) and three temperature levels (27, 43, and 6 C) were chosen. The treatment time at 6 C for pressure levels of 5 and 6 MPa was of very short duration, i.e., in the order of seconds, which was beyond the accuracy and precision level control of the HPP system. So, in total seven treatment conditions were performed for evaluating the inactivation and injury of L. monocytogenes in UHT whole milk immediately after the treatment. For each combination, six treatment times (pressure hold time is used to designate the treatment time) were chosen. Following each treatment, cell counts were obtained on nonselective (TSAYE) and selective (MOX) media to determine the healthy plus injured and only healthy cells, respectively. The inactivation of the microorganism was evaluated in terms of the log reduction values. Plating the milk samples on selective and nonselective media revealed that high treatment temperature promoted the inactivation of L. monocytogenes. For instance, on TSAYE media at 4 MPa and 6 C, 6 log 1 reduction was obtained in 1 sec; whereas, at 43 C and 27 C the maximum reduction obtained was 4.17 and 2.59 log 1 at treatment times of 18 and 3 min, respectively. At a constant treatment temperature the effect of increasing the pressure was also evident. For example, on TSAYE media at 43 C, approximately 4 log 1 reduction was obtained for 4 MPa and 18 min treatment time; whereas, the same log reduction was obtained at 5 and 6 MPa for treatment time of 3 min and 4 sec, respectively. As the treatment times for the seven combinations of pressure and temperature were different, the effectiveness of the treatments was evaluated by comparing the D-values of all the combinations by Tukey s pairwise comparison. Tukey s pairwise comparison on both media showed that the D- values of treatment condition 4 MPa, 27 C and 4 MPa, 43 C were significantly different from each other (p <.5) and from the rest of the five pressure and temperature combinations. Furthermore, the remaining five combinations were not significantly different from each other (p >.5). To determine the effect of plating media for a particular combination of pressure and temperature, the D-values obtained for both the media were also compared by Tukey s pairwise comparison. The comparisons showed iv

5 that for 4 MPa, 27 C and 6 MPa, 27 C there was no significant difference (p >.5) in the D-values of both the media; whereas, for rest of the five combinations the D-values were significantly different (p <.5). Mathematical representations for log reduction vs. time were obtained for both TSAYE and MOX media using the conventional two-parameter Weibull model. The R 2 values obtained for TSAYE and MOX media were from.96 to.99 showing the twoparameter Weibull model was the best fit. However, it was found that the value of the parameter α (i.e., characteristic time) did not satisfy the physical interpretation. So Weibull model, with α predetermined, was then fitted to the log reduction values obtained on the TSAYE and MOX media. Predetermining the α value from expereimental data reduced the two-parameter Weibull model to one-parameter model. Additionally, by following this approach a trend for parameters α and β was found for the first time for all the treatment conditions. Although, the one-parameter Weibull model represented the correct interpretation of α, the R 2 value of the predicted curve decreased (range was from.81 to.97 for TSAYE media and.82 to.95 for MOX media). So a four-parameter Weibull model with two characteristics times (α) and two curvatures (β) were fitted to the log reduction values which improved the R 2 values (range was from.95 to.99 for both TSAYE and MOX media) of the predicted curve. The log reduction of injured cells was calculated at seven combinations of pressure and temperature by using the log reduction values obtained on the nonselective TSAYE) and selective (MOX) media. Like inactivation, the effect of pressure and temperature was also evident on log reduction of injured population. For instance, the maximum reduction of injured cells at 4 MPa and the three temperatures 27, 43, and 6 C were 2.72, 4.18, and 4.58 log 1 for treatment times of 3, 18, and 1 min, respectively. This clearly shows the effect of temperature. Similarly, at 27 C the maximum reduction of injured cells obtained at three pressure levels of 4, 5, and 6 MPa were 2.72, 5.14, and 5.52 log 1 for treatment time 3, 6, and 2 min, respectively. The predicted injury curve was established by taking the difference between the Weibull model developed for the log reduction vs. time for the TSAYE and MOX media. v

6 Predicting the injury curve from the one-parameter Weibull model resulted in decreased R 2 values (range was from to 9) compared to the two-parameter (range was from.77 to.99) Weibull model including negative R 2 value. This problem was overcome by predicting the injury curve using the four-parameter Weibull model (range of R 2 values was from.66 to.95). Micro-differential scanning calorimetry (mdsc) study of UHT whole milk, inoculated UHT whole milk, and high pressure and temperature-treated inoculated UHT whole milk samples were done using a VP-DSC MicroCalorimeter. The purpose of the study was to determine if mdsc results can sufficiently differentiate between the untreated inoculated and treated inoculated whole milk samples with respect to the amount of cellular proteins content. Since sufficient differentiation between the inoculated untreated and inoculated treated UHT milk was not observed, mechanistic features could not be incorporated into the combined predictive model for injury and recovery. With the purpose of developing a combined model for injury and recovery following the high pressure and temperature treatment one combination of pressure and temperature was chosen, i.e., 4 MPa, 6 C and 5 sec, which was also considered for the mdsc study. After the treatment, milk samples were kept for 3 days at 4 C and plated at 2-day intervals on TSAYE and MOX media to evaluate injury and recovery. However, upon analysis it was found that complete recovery occurred between day 6th and 8th. Hence the predictive model was developed by taking the data upto day 6th. The different terms of the predictive model are N t * (population of microorganisms on TSAYE plate after time t following the HPP treatment), I t * (population of injured microorganisms after time t following the HPP treatment), G t (population of microorganisms on growth curve at time t (initial inoculum of the growth curve = H *; H *, being the population of microorganisms on MOX plate immediately after the HPP treatment), R t (population of recovered microorganisms after time t following the HPP treatment). The observed data for each term was described by a mathematical model and used for validation of the predictive model. To the best of our knowledge this is the first study on the development vi

7 of a combined predictive model for bacterial injury and recovery under high pressure and temperature conditions. The predictive model enables the determination of the population of total, healthy, injured, and recovered cells at any time during the storage of milk product at 4 C following HPP treatment. In addition, this model can also help to estimate the product shelf-life and assess risk due to the recovery of the pathogenic microorganisms during the storage period. The procedure for development of this model is expected to be helpful in advancement of the field of predictive microbiology. vii

8 TABLE OF CONTENTS LIST OF FIGURES... xvii LIST OF TABLES... xxiii ACKNOWLEDGEMENTS...xxv 1. CHAPTER INTRODUCTION LITERATURE REVIEW Listeria monocytogenes General characteristics and growth conditions Ecology Listeriosis L. monocytogenes and milk Milk Milk processing technologies Pasteurization Pulsed electric field Ultrasound Ultraviolet light Pulsed UV-light High pressure processing Principles of high pressure processing of food Description of HPP process Batch HPP process Semi-continuous HPP process...13 viii

9 Adiabatic heating during HPP Critical process factors influencing effectiveness of HPP treatment on microorganisms Type of microorganisms Composition of food material Water activity (a w ) ph Treatment conditions Mechanisms of inactivation Cell membrane and cell wall Biochemical reactions Genetic mechanism Injury and recovery of microorganisms Predictive microbiology for microorganisms Introduction and importance of predictive microbiology Primary, secondary, and tertiary models Growth models Monod model Gompertz model Logistic model Baranyi model Buchanan three- phase linear model Inactivation/survival model First order model...28 ix

10 Log Logistic model Modified Gompertz model Weibull model Inactivation models under high pressure and/or temperature treatments Inactivation models for high pressure Inactivation models under high pressure and temperature treatment Injury and recovery models Importance and need for present research Injury and recovery of microorganisms under high pressure Study of physiological changes in microorganisms caused by high pressure using DSC and other methods Summary CHAPTER - GOALS, OBJECTIVES, AND HYPOTHESES CHAPTER - MATERIALS AND METHODS Overview Phase -1 Generation of inactivation data immediately after combined high pressure and temperature treatment Preparation of inocula Preparation of milk samples for high pressure and temperature treatment High pressure and temperature treatment of samples Description of high pressure unit Experimental protocol for high pressure and temperature treatment of the samples Microbiological analysis Statistical analysis...55 x

11 Modeling the survivors on nonselective and selective media Goodness of fit comparison R MSE Weibull model parameter determination Weibull model parameter comparison Scale factor (α) Shape factor (β) Reliable life (t R ) Phase -2 Calculation and modeling the population of injured cells Phase -3 An approach to differentiate physiologically the healthy, injured, and recovered cells of L. monocytogenes using Micro Differential Scanning Calorimetry (mdsc) Preparation of inocula Preparation of milk sample for high pressure and temperature treatment Microbial analysis mdsc analysis of sample Comparison of treated and untreated inoculated milk samples Determination of mass of milk Conversion of DSC output (differential heat flow) in cal/ C to kj/ kg K Determination of specific heat of milk Conversion of DSC output (differential heat flow) from water as reference cell to empty pan as a reference cell...65 xi

12 4.5. Phase 4 Development and validation of the post processing model/combined model for injury and recovery of L. monocytogenes HPP treatment for development of the post processing model Validation of the inactivation model developed for TSAYE and MOX data * for 4 MPa and 6 C and prediction of N and H * Modeling the total population of cells (N t *) on nonselective media following HPP treatment Modeling the population of healthy cells (H t *) on selective media following HPP treatment Determination and modeling of population injured cells (I t *) Determination and modeling of population of recovered cells (R t ) Modeling the growth of untreated cells (G t ) with initial population of ~1 2 CFU/ml Calculation of population of recovered cells Modeling the population of recovered cells Development of the post processing model Validation of the post processing model CHAPTER- RESULTS AND DISCUSSION: MODELING THE SURVIVAL CURVES OF L. monocytogenes Inactivation test results Effect of temperature on inactivation At 4 MPa pressure and three treatment temperature levels On nonselective media at 27, 43, and 6 C On selective media at 27, 43, and 6 C...74 xii

13 At 5 MPa pressure and two treatment temperature levels On nonselective media at 27 and 43 C On selective media at 27 and 43 C At 6 MPa pressure and two treatment temperature levels On nonselective media at 27 and 43 C On selective media at 27 and 43 C Effect of pressure on inactivation At 27 C treatment temperature and three pressure levels At 43 C treatment temperature and three pressure levels Effect of plating media Statistical analysis D-values comparisons Modeling the survivors on nonselective and selective media with Log-linear and two- parameter Weibull model Goodness-of-fit comparison Weibull model fitting results Weibull model parameters Scale factor (α) Shape factor (β) Reliable life (t R ) Modeling the survivors on nonselective and selective media with constrained α (one-parameter Weibull model) Modeling the survivors on nonselective and selective media with four-parameter Weibull model xiii

14 5.1. Relationships between D, α, and β vs. treatment pressure and temperatures The effect of pressure and temperature on D, α, and β values on nonselective media (TSAYE) The effect of pressure and temperature on D, α, and β values on selective media (MOX) CHAPTER RESULTS AND DISCUSSION: MODELING THE POPULATION OF INJURED CELLS OF L. monocytogenes Injury test results Effect of temperature on injury At 4 MPa and three treatment temperature levels At 5 MPa and two temperature levels At 6 MPa and two temperature levels Effect of pressure on injury Modeling the population of injured cells Modeling population of injured cells using two-parameter Weibull model Modeling population of injured cells using one-parameter Weibull model (constrained α) Modeling the population of injured cells using four-parameter Weibull model CHAPTER RESULTS AND DISCUSSION: MICRODIFFERENTIAL CALORIMETER (mdsc) EXPERIMENTAL RESULT High pressure treatment results for two different test volumes for HPP mdsc test results UHT whole milk L. monocytogenes inoculated UHT whole milk xiv

15 HPP treated inoculated milk Comparison of DSC treated samples Temperature range of 5 to 1 C Difference between whole milk and untreated inoculated milk Difference between untreated inoculated mted inoculated milk Temperature range of 1 to 1 C Difference between whole milk and untreated inoculated milk Difference between untreated inoculated and treated inoculated milk CHAPTER RESULTS AND DISCUSSION: DEVELOPMENT AND VALIDATION OF POST PROCESSING MODEL FOR INJURY AND RECOVERY OF L. monocytogenes High pressure and temperature treatment Prediction of 5 sec TSAYE and MOX inactivation data using Weibull model Modeling of total population of cells (N t *) on nonselective media following HPP treatment Modeling of healthy cells (H t *) on selective media following HPP treatment Modeling of injured cells (I t *) Determination and modeling of population of recovered cells Modeling the growth of untreated cells (G t ) with initial inoculum of ~1 2 CFU/ml Calculation of population of recovered cells (R t ) Development of the post processing model Validation of the post processing model xv

16 9. CHAPTER- CONCLUSIONS AND RECOMMENDATIONS FOR FUTURE STUDY Inactivation study Injury study mdsc study Development and validation of the post processing model for injury and recovery Recommendations for future work REFERENCES APPENDIX A: SAS CODE FOR STATISTICAL ANALYSIS...19 APPENDIX B: MATLAB CODE FOR FOUR-PARAMETER WEIBULL MODEL APPENDIX C: IMPROVED R 2 VALUES OF THE PREDICTED INACTIVATION CURVE BY FOUR-PARAMETER WEIBULL MODEL APPENDIX D: WRITE UP ON DSC...22 xvi

17 LIST OF FIGURES Figure 2.1. Flow diagram of high pressure processing (FAO, 21)...12 Figure 2.2. Effect of sublethal treatments on microbial cells (Ray, 1979, 1989; Russel 1984; McFeters, 1989; Bozoglu et al., 24)...2 Figure 2.3. A schematic of a typical prokaryotic cell (Flu Wiki, 27, with permission) 39 Figure 4.1. High pressure processing unit...49 Figure 4.2. Typical pressure and temperature profile for 4 MPa, 27 C, and 3 min treatment time...5 Figure 4.3. Typical pressure and temperature profile for 4 MPa, 43 C, and 18 min treatment time...51 Figure 4.4. Typical pressure and temperature profile for 4 MPa, 6 C, and 1 sec treatment time...51 Figure 4.5. Typical pressure and temperature profile for 5 MPa, 27 C, and 6 min treatment time...52 Figure 4.6. Typical pressure and temperature profile for 5 MPa, 43 C, and 6 min treatment time...52 Figure 4.7. Typical pressure and temperature profile for 6 MPa, 27 C, and 12 sec treatment time...53 Figure 4.8. Typical pressure and temperature profile for 6 MPa, 43 C, and 1 sec treatment time...53 Figure 5.1. Inactivation of L. monocytogenes at 4 MPa and three temperature levels on nonselective media...74 Figure 5.2. Inactivation of L. monocytogenes at 4 MPa and three temperature levels on xvii

18 selective media...75 Figure 5.3. Inactivation of L. monocytogenes at 5 MPa and two temperature levels on nonselective media...76 Figure 5.4. Inactivation of L. monocytogenes at 5 MPa and two temperature levels on selective media...77 Figure 5.5. Inactivation of L. monocytogenes at 6 MPa and two temperature levels on nonselective media...78 Figure 5.6. Inactivation of L. monocytogenes at 6 MPa and two temperature levels on selective media...79 Figure 5.7. Inactivation of L. monocytogenes at 27 C and three pressure levels on nonselective media...8 Figure 5.8. Inactivation of L. monocytogenes at 27 C and three pressure levels on selective media...8 Figure 5.9. Inactivation of L. monocytogenes at 43 C and three pressure levels on nonselective media...81 Figure 5.1. Inactivation of L. monocytogenes at 43 C and three pressure levels on selective media...82 Figure Effect of plating media on log reductions of L. monocytogenes following combined high pressure and temperature treatment at 4 MPa and 27, 43, and 6 C...83 Figure Effect of plating media on log reductions of L. monocytogenes following combined high pressure and temperature treatment at 5 MPa. and 27, 43 C...83 Figure Effect of plating media on log reductions of L. monocytogenes following xviii

19 combined high pressure and temperature treatment at 6 MPa and 27, 43 C...84 Figure Residual analysis of the inactivation of L. monocytogenes by a) Log-linear model, b) Weibull model...9,91 Figure Correlation between the observed and predicted values a) Log-linear model (R 2 =.94, slope =.948, b) Weibull model (R 2 =.98, slope =.98)..91, 92 Figure Inactivation curves of L. monocytogenes at seven combination of pressure and temperature fitted with two-parameter Weibull model (a) 4 MPa and 27 C, TSAYE, (b) 4 MPa and 27 C, MOX, (c) 4 MPa and 43 C TSAYE, (d) 4 MPa and 43 C, MOX, (e) 4 MPa and 6 C, TSAYE, (f) 4 MPa and 6 C, MOX, (g) 5 MPa and 27 C, TSAYE, (h) 5 MPa and 27 C, MOX, (i) 5 MPa and 43 C, TSAYE, (j) 5 MPa and 43 C, MOX, (k) 6 MPa and 27 C, TSAYE, (l) 6 MPa and 27 C, MOX (m) 6 MPa and 43 C, TSAYE, (n) 6 MPa and 43 C, MOX...93, 94, 95 Figure Effect of temperature on α value of Weibull model at a constant pressure (4 MPa, R 2 =.99)...97 Figure Inactivation curves of L. monocytogenes at seven combination of pressure and temperature fitted with one-parameter Weibull model (a) 4 MPa and 27 C, TSAYE, (b) 4 MPa and 27 C, MOX, (c) 4 MPa and 43 C, TSAYE, (d) 4 MPa and 43 C, MOX, (e) 4 MPa and 6 C, TSAYE, (f) 4 MPa and 6 C, MOX, (g) 5 MPa and 27 C, TSAYE, (h) 5 MPa and 27 C, MOX, (i) 5 MPa and 43 C, TSAYE, (j) 5 MPa and 43 C, MOX, (k) 6 MPa and 27 C, TSAYE, (l) 6 MPa and 27 C, MOX, (m) 6 MPa and 43 C, TSAYE, (n) 6 MPa and 43 C, MOX...14,15, 16 xix

20 Figure Survival data on nonselective medium (TSAYE) plotted against (t/α) at 27 C for the three pressure level with unconstrained α...19 Figure 5.2. Survival data on nonselective medium (TSAYE) plotted against (t/α) at 27 C for the three pressure level with constrained α...19 Figure Graph showing the contribution of the two terms in the four-parameter Weibull model Figure 6.1. Log reductions of total, healthy and injured cells of L. monocytogenes at 4 MPa (a) 27 C (b) 43 C (C) 6 C...121,122 Figure 6.2. Log reductions of total, healthy and injured cells of L. monocytogenes at 5 MPa (a) 27 C (b) 43 C Figure 6.3. Log reductions of total, healthy and injured cells of L. monocytogenes at 6 MPa (a) 27 C (b) 43 C Figure 6.4. Effect of temperature on injury at constant pressure level (a) 4 MPa, (b) 5MPa, (c) 6 MPa...125,126 Figure 6.5. Effect of temperature on injury at constant pressure level (a) 27 C, (b) 43 C127 Figure 6.6. Injury curve of L. monocytogenes obtained using Weibull model with unconstrained α (a) 4 MPa and 27 C, (b) 4 MPa and 43 C, (c) 4 MPa and 6 C, (d) 5 MPa and 27 C, (e) 5 MPa and 43 C, (f) 6 MPa and 27 C, (g) 6 and MPa and 43 C...13, 131 Figure 6.7. Injury curve of L. monocytogenes obtained using Weibull model with constrained α (a) 4 MPa and 27 C, (b) 4 MPa and 43 C, (c) 4 MPa and 6 C,(d) 5 MPa and 27 C, (e) 5 MPa and 43 C, (f) 6 MPa and 27 C, (g) 6 MPa and 43 C...132, 133 xx

21 Figure 6.8. Predicted injury curve at different (α 1 / α 2 ) ratio for 4 MPa and 6 C Figure 7.1. Thermograms of UHT whole milk (a) 5 to 1 C and (b) 1 to 1 C.14, 141 Figure 7.2. Thermograms of UHT whole milk inoculated with L. monocytogenes (a) 5 to 1 C and (b) 1 to 1 C Figure 7.3. Thermograms of high pressure and temperature treated (4 MPa, 6 C, 5 sec) UHT whole milk inoculated with L. monocytogenes (a) 5 to 1 C and (b) 1 to 1 C Figure 7.4. Difference in the heat flow values of treated inoculated and untreated inoculated whole milk with temperature Figure 8.1. Flow diagram showing different sections of Chapter Figure 8.2. Experimental average values (points) and Gompertz model fitted curve (line) for TSAYE data following the HPP treatment at 4 MPa and 6 C for 5 sec, incubated at 4 C over a period of 3 days Figure 8.3. Experimental average values (points) and Gompertz model fitted curve (line) for MOX data following the HPP treatment at 4 MPa and 6 C for 5 sec, incubated at 4 C over a period of 3 days Figure 8.4. Plot of log N t *, log H t *, and log G t with time (In the figure, G t is the growth curve of untreated L. moncytogenes in whole milk at 4 C over a period of 3 days with an initial inoculum same as the population of healthy cells (H * ) immediately after high pressure and temperature treatment of 4 MPa and 6 C for 5 sec) Figure 8.5. Data and mathematical model values for I t * (in CFU/ml), upto day 6 th Figure 8.6. Data and mathematical model values for It*(in log1 values), upto day 6 th 158 Figure 8.7. Data and mathematical model values for I t *(in CFU/ml), upto day 8 th xxi

22 = 1 CFU/ml Figure 8.8. Data and mathematical model values for I t *(in log 1 values), upto day 8 th = 2 log Figure 8.9. Experimental average growth data (points) of untreated cells and Gompertz model fitted curve (line) over a period of 3 days...16 Figure 8.1. Experimental average number of recovered cells with respect to day vs time Figure Types of injury curves, Hills and Mackey (1995) Figure Types of recovery curves, Hills and Mackey (1995) Figure Trends of I t * and R t with time in arbitrary scale Figure A flow diagram to show different steps for development and validation of the post processing model...17, 171 xxii

23 LIST OF TABLES Table 2.1. Listeriosis outbreaks and occurrences of L. monocytogenes in milk...7 Table 4.1. Experimental design for inactivation and injury experiments...54 Table 4.2. Initial and day 2 data on TSAYE and MOX plates following HPP treatment.69 Table 5.1. D-values on nonselective medium (TSAYE) at different treatment conditions86 Table 5.2. D-values on selective medium (MOX) at different treatment conditions...87 Table 5.3. D-values comparisons of nonselective medium (TSAYE) and selective medium (MOX) at each treatment condition...88 Table 5.4. Comparison of goodness-of-fit of Weibull and Log linear models on TSAYE data...89 Table 5.5. Comparison of goodness-of-fit of Weibull and Log linear models on MOX data...9 Table 5.6. Weibull model parameters for different treatment condition...96 Table 5.7. Weibull model parameters with constrained α for different treatment condition...13 Table 5.8. Trend of α and β at constant pressure and temperature with constrained and unconstrained α for nonselective medium (TSAYE)...17 Table 5.9. Trend of α and β at constant pressure and temperature with constrained and unconstrained α for selective medium (MOX)...18 Table 5.1. Output of MATLAB program for 4 MPa and 27 C (TSAYE) at different α 1 / α Table 6.1. R 2 values of the predicted injury curve Table 6.2. Improved injury curve by taking different (α 1 / α 2 ) ratio at 4 MPa xxiii

24 and 6 C Table 6.3. Improved injury curve by taking different (α1/ α2) ratio at 5 MPa and 43 C Table 8.1. Observed and predicted data in log scale for TSAYE plate at 5 sec treatment time Table 8.2. Observed and predicted data in log scale for MOX plate at 5 sec treatment time Table 8.3. Comparison of observed N t * with sum of observed I t *, R t, and G t Table 8.4. Comparison of observed N t * with sum of predicted I t *, R t, and G t Table 8.5. Comparison of observed N t *, sum of observed I t *, R t, and G t and, sum of predicted I t *, R t, and G t in log scale Table 8.6. Comparison of sum of observed (I t */ I *) and observed (R t / I *) with Table 8.7. Comparison of sum of predicted (I t */ I *) and predicted (R t / I *) with xxiv

25 ACKNOWLEDGEMENTS I would like to gratefully acknowledge my advisor, Dr. Virendra M. Puri for, his invaluable guidance throughout my study. I feel honored to be his student and I am grateful for his constant motivation, encouragement and moral support during my Ph. D. research. I am grateful to Dr. Ali Demirci, Dr. Ramaswamy C. Anantheswaran, and Dr. Stephen J. Knabel for serving on my advisory committee and their support and guidance. My special thanks go to Dr. Katherine L. Bialka, Dr. Melinda Hayman, and Dr. Ibrahim Gulseren for their professional support at different stages of my research. I am thankful to Dr. Durland L. Shaumway from the Statistics Consulting Center for helping me with the statistical analysis. I would like to thank all my friends and colleagues in the Department of Agricultural and Biological Engineering for their support during my graduate study. I express my deepest love and gratitude to my husband, Kamala Ballava Dash and my daughter Nishka Dash. The process of completing this research has been a big challenge for all three of us and I would not have succeeded without your love, support and encouragement. Thank you for supporting and encouraging me. Finally, I would like to dedicate this thesis to my mother Mrs. Santosini Dash, who has been my inspiration throughout my life. I am deeply thankful to you for your unconditional love, support and everything that you have done for me to make me a better person in life. xxv

26 CHAPTER 1 INTRODUCTION Among the nonthermal food processing technologies, high pressure processing (HPP) has been known to be a potential food preservation technique for over a hundred years. High pressure has been applied during the twentieth century for production of medicine, electronic components, machine parts, and consumer products in pharmaceutical, ceramics, powdered metals, and refractory industries (Palou et al., 22). Hite (1899) reported the first use of high pressure in preservation of food. However, the technology to process food using HPP on a commercial scale did not come to fruition until recently. The first high pressure treated food product, a high acid jam was produced by the Japanese in 199. Several high pressure treated food product such as, yogurts, food jellies, salad dressings, and fruit sauces were also introduced in Japanese market in 1991 (Williams, 1994). Pressure treated foods have been commercially available in the U.S. market since mid 199s. Food products available to the consumer that currently employ high pressure processing in their manufacturing include guacamole, oysters, ham, fruit jellies and jams, fruit juices, pourable salad dressings, salsa, poultry, and rice products. Many studies on high pressure treated milk and dairy product have been performed, which have led to the development of new products and improvements in the safety of current products (Guamis et al., 25). Listeria monocytogenes is a Gram positive, nonspore forming, facultatively anaerobic, rod-shaped bacterium, which can grow in the temperature range of -.4 C to 5 C and ph range of 4.1 to 9.6 (Chen and Hoover, 23a). L. monocytogenes has been responsible for several outbreaks of foodborne illnesses. Each year approximately 2,5 individuals become seriously ill due to L. monocytogenes infections, and nearly 5 of these die from their infection in the United States (CFSAN, 25). Among the illness caused by foodborne diseases, Listeriosis only accounts for.2% but it causes 27.6% of all deaths due to foodborne infection (CFSAN, 25). The main victims of listeriosis are 1

27 pregnant women, newborne babies, and persons with weak immune systems. L. monocytogenes has been found in many foods such as uncooked meats and vegetables and in processed foods such as soft cheeses and cold cuts at the deli counter, that are contaminated after processing. Unpasteurized milk or food made from unpasteurized milk may contain this bacterium. In an outbreak of listeriosis in Massachusetts in 1983, pasteurized whole or 2% milk was determined to be the source of infection (CFSAN, 25). A large outbreak occurred in 1985 in Los Angeles County affecting 93 pregnant women and killing 48 people. Adulterated Mexican-style cheese was the cause of illnesses in this outbreak (CFSAN, 25). In 2, there was an outbreak of listeriosis among Hispanic persons living in the Winston-Salem area of North Carolina affecting 13 people. The outbreak was caused by the consumption of non-commercial, homemade, Mexican-style cheese produced from contaminated raw milk sold to unlicensed cheese makers by a local dairy (CFSAN, 25). Milk and dairy products are a rich source of macronutrients, proteins, fats and carbohydrates; they are a very important source of calcium and riboflavin and provide significant amounts of most other essential nutrients. Milk and dairy products are an important part of the diet of many consumers in many countries. More than nine million dairy cows are present in the United States, dairy farming being a diverse industry in America. The U.S. and many other countries are supplied with billions of pounds of dairy products each year by 65, working dairy farms in the U.S. (BFN, 26). The annual milk production in the year 2 was billion pounds, i.e., worth more than $2 billion (IDFA, 21). Milk is produced in large quantities in all 5 States of the USA. The top ten milk producing states are California, Wisconsin, New York, Pennsylvania, Minnesota, Idaho, Texas, Michigan, Washington, and New Mexico (ERS, 24). The dairy industry plays a vital role in the U.S. economy. Both the large and small dairy farms present in the U.S. contribute to the local economy by supporting local businesses and the community tax base. A strong dairy industry helps to enhance both the agricultural economy and the economic well being of America's rural communities (BFN, 26). So milk and dairy products are important from a nutritional, as well as an economical, point of view in the U.S. 2

28 L. monocytogenes is a potential threat to milk and dairy products causing many outbreaks and deaths of people in the U.S. Hence efforts are needed for increasing the shelf-life and safety of milk and dairy products by estimating the growth, inactivation, injury, and recovery of L. monocytogenes after different processing conditions. In recent years, many studies have been carried out on the inactivation of pathogenic and spoilage microorganisms (naturally present or inoculated) by high pressure in milk. These analyses have generally demonstrated that raw milk with a microbiological quality comparable to that of pasteurized (72 C, 15 sec) milk can be obtained by using pressure treatment of 4 to 6 MPa (Kolakowski et al., 1997; Mussa and Ramaswamy, 1997; Buffa et al., 21). Several studies have been carried out on the effects of high pressure on inoculated target microorganisms, with the aim of determining the sensitivity of pathogenic and spoilage microorganisms in milk (Trujillo et al., 22). L. monocytogenes is a very hardy organism, and shows resistance to high pressure processing. Styles et al. (1991) compared the pressure inactivation of L. monocytogenes Scott A in both raw and ultra high temperature (UHT) milk with its inactivation in 1 mm PBS (phosphate buffered saline) with ph 7.. When treated at 34 MPa for 2 min, it was found that L. monocytogenes was more resistant in both UHT and raw milk with only 2 log 1 reductions compared to 7 log 1 reductions in PBS. In a similar study, Simpson and Gilmour (1997) compared the inactivation of two strains of L. monocytogenes (NCTC 11994, Lm1 and a poultry isolate, Lm2) in (UHT) treated milk, raw chicken mince, cooked chicken mince and cooked beef mince at 375 MPa pressure and ambient temperature for 3 min. They found that both strains of L. monocytogenes were more resistant in UHT milk. Therefore, it is important to study and characterize the resistance of L. monocytogenes in milk under combined effects of high pressure and temperature. Predictive microbiology combines elements of microbiology, mathematics, and statistics to develop models that mathematically describe and predict the growth and death of microorganisms that have undergone some specific environmental conditions (Whiting, 1995). In predictive microbiology, microbial responses are measured under 3

29 controlled environment. The results of microbial responses are expressed in mathematical equations, which can predict the microbial responses at different conditions by interpolation (Ross and McMeekin, 1994). This approach enables one to: predict the risks involved during the storage of product which in turn helps to redesign the production process for meeting required levels of safety and shelflife; improve the Hazard Analysis and Critical Control Point (HACCP) approach after proper evaluation of the different steps in the processing operation; evaluate the risks and consequences in process and storage control (Ross and McMeekin, 1994). At present, only a few predictive models related to inactivation kinetics of L. monocytogenes under the combined effects of temperature and pressure are known. Though injury and recovery models were found related to heat treatment alone and pressure treatment alone, no model is presently available for predicting the combined effects of temperature and pressure. Hence, development of these models is necessary to predict the shelf-life and optimum safe process conditions of food products. Consumers are now increasingly very concerned about the safety of foods. L. monocytogenes is one of the most important foodborne pathogens and is responsible for several outbreaks of milk and dairy products in the U.S. It is a very hardy organism and very resistant to high pressure treatment of milk. To make the process more effective, as mentioned in preceding paragraphs, researchers have studied the inactivation kinetics of L. monocytogenes under the combined effects of temperature and pressure. Since no predictive models for L. monocytognes under the combined effects of temperature and pressure could be found, it is important to develop injury and recovery models, in addition to inactivation models for this important pathogen. A validated predictive model can be implemented for better estimation of shelf-life and safety of milk as L. monocytogenes can recover and grow after pressure and temperature treatment. 4

30 CHAPTER 2 LITRATURE REVIEW This literature review summarizes the response of L. monocytogenes in milk under high pressure and temperature. In addition, various models related to growth, inactivation, injury, and recovery of L. monocytogenes and other microoraganisms under different environmental conditions are reviewed Foodborne diseases are caused by consumption of the contaminated foods. More than 25 known foodborne diseases are known (CDC, 25). Foodborne diseases cause approximately 76 million illnesses, 325, hospitalizations, and 5, deaths in the United States each year (CDC, 1999). Most of these foodborne diseases are infections, caused by a variety of bacteria, viruses, and parasites. The common bacterial species responsible for foodborne illnesses are Bacillus cereus, Campylobacter jejuni, E. coli 157: H7, Listeria monocytogenes, and Salmonella enterica (CDC, 25) Listeria monocytogenes General characteristics and growth conditions L. monocytogenes is a Gram positive, nonspore forming, facultative anaerobic, rod-shaped bacterium with diameter of.4 to.5 µm and length of.5 to 2. µm. L. monocytogenes can grow between temperatures of -.4 C and 5 C (Farber and Peterkin, 1991) with optimum growth temperature of 3 C to 37 C. L. monocytogenes is a very hardy microorganism which can grow in a ph range of 4.1 to 9.6 and can grow at low a w of.9 (Chen and Hoover, 23a) Ecology L. monocytogenes is widely distributed in nature, and commonly found in soil, water, and on decayed plant material such as silage. L. monocytogenes can grow rapidly on decayed green vegetation and has been suspected as the source of listeriosis in farm animals, which may also be the cause of spreading contamination along the food chain. The survival, growth, and multiplication of L. monocytogenes under adverse 5

31 environmental conditions such as different food processing environments make it a potential threat to the food industry (Fenlon, 1999) Listeriosis The outbreaks due to Listeriosis are assumed to be due to the consumption of food contaminated with L. monocytogenes (Farber and Peterkin, 1991). The bacterium has been found in different food products such as uncooked meats and vegetables. In addition, in processed foods such as soft cheeses and cold cuts at the deli counter and hot dogs, which were contaminated after processing during the packaging step. Unpasteurized (raw) milk or foods made from unpasteurized milk may contain the bacterium (CDC, 29). The majority of human cases of listeriosis occur in individuals who have an underlying condition that leads to suppression of their T-cell mediated immunity (Farber and Peterkin, 1991). The main victims of listeriosis are pregnant women, newborn babies and people with weakened immune systems (CDC, 29). Each year approximately 2,5 people become seriously ill due to L. monocytogenes infections and nearly 5 of these die from their infection in the United States (CDC, 29). Listeriosis only accounts for.2% of illness due to foodborne diseases, but it causes 27.6% of all deaths due to food borne infection (CFSAN, 25) L. monocytogenes and milk L. monocytogenes is psychrotrophic in nature and thus has the ability to grow under refrigeration temperatures. Since refrigeration is the only method of preserving fluid milk and milk products for long time, the period of storage is a period of enrichment for L. monocytogenes. Several outbreaks of listeriosis have been traced to the consumption of contaminated raw milk, pasteurized milk, and many dairy products. Table 2.1 summarizes the outbreaks due to listeriosis. 6

32 Table 2.1. Listeriosis outbreaks and occurrences of L. monocytogenes in milk Year Location Source Deaths (miscarrages) References 1983 Massachusetts Pasteurized milk 49 (14) Fleming et al.(1985) 1985 California Jalicose cheese 142 (48) Sutherland and Porritt (1997) 1986 Massachusetts Raw milk 36 (16) Hays et al. (1986) 1987 Los Angeles Butter 11() Lovett et al. (1987) 1994 USA Pasteurized chocolate milk 56 () Sutherland and Porritt (1997) 2 North Carolina Mexican-style cheese 13 () CDC (21) 25 USA Mexican-style cheese 6 () CDC (25) 27 Massachusetts Pasteurized milk 3(1) CDC (28) 2.2. Milk Milk and dairy products are an important part of the diet of consumers in many countries. Milk contains most of the nutrients required by humans. It is composed of 87.95% water, 4.64% carbohydrate (lactose), 3.71% fats, 3.22% protein, and.72% mineral ash (Harper and Hall, 1976). The U.S. and many other countries are supplied with billions of pounds of dairy products each year by 65, working dairy farms in the U.S. (BFN, 26). The annual milk production in the year 2 was billion pounds, worth more than $2 billion (IDFA, 21). The high moisture content and neutral to slightly acidic ph range of milk make it an excellent growth medium for various microorganisms. Raw milk can easily become 7

33 contaminated with pathogenic microorganism such as Escherichia coli, Salmonella, Campylobacter jejuni, Yersinia enterocolitica, Brucella meliterisis, L. monocytogenes. The milkborne diseases include anthrax, typhoid fever, scarlet fever, tuberculosis, diphtheria, and foot and mouth disease (Holsinger et al., 1999). Milkborne diseases accounted for about 25% of illness associated with food and water in the U.S. until 1938 (Holsinger et al., 1999). The storage period of milk is very short without any preservatives or preservation treatments. Since preservatives are not added, raw milk must receive a preservation treatment to enhance the shelf-life of milk and prevent disease. In the following section, the conventional and several novel preservation treatments/ technologies used for milk processing are described Milk preservation technologies Pasteurization The conventional way of preserving milk is by pasteurization. According to the treatment temperature and the processing time, there are three types of pasteurization technologies. The three different pasteurization technologies are 1) Low temperature-long time/ LTLT (at 62 C for 3 min), 2) high temperature-short time /HTST (71.7 C for 15 sec), 3) Ultra high temperature/ UHT (137.8 C for 2 sec) (Varnam and Sutherland, 1994). Although pasteurization is an effective method to inactivate microorganism, it has many disadvantages. For example, there is loss in vitamins, proteins, lactose, and minerals during the heating (Varnam and Sutherland, 1994). Also, the structure and quality of milk changes due to: protein denaturation, enzyme inactivation, lipid oxidation, and non- enzymatic browning. The presence of pathogenic microorganisms such as, L. monocytogenes, Y. enterocolitica, S. aureus, and Bacillus cereus in pasteurized milk may be due to underprocessing post-processing contamination (Varnam and Sutherland, 1994). To overcome these limitations of the conventional pasteurization method new technologies have been developed over time for milk processing. Some alternative 8

34 techniques used for milk processing such as, pulsed electric fields, ultrasound ultravioletlight, and high hydrostatic pressure are described in the following sections Pulsed electric field Pulsed Electric Field (PEF) processing involves treating the food materials by high voltage electric pulses in the range of 2 to 8 kv (Ohio, 21). The applied voltage results in an electric field which inactivates the microorganism by electroporation and disruption of semi-permeable membranes resulting in swelling and rupture of the cell membrane (Ohio, 21). PEF technology has been successfully used to inactivate pathogens in liquid food products. For example, a 3 log 1 reduction of L. monocytogenes was obtained at 25 C and a 4 log 1 reduction at 5 C by a continuous flow PEF system in milk (Reina et al., 1998) Ultrasound Ultrasound technology is an emerging food processing technology which uses acoustic/sound waves having a frequency above the human hearing range i.e., above 2 khz to inactivate microorganisms. Microbial inactivation by ultrasound is attributed to intracellular cavitation (Hughes and Nyborg, 1962). Cavitation is the process of formation, growth and collapse of microscopic bubbles induced by fluctuating pressure in the medium during ultrasonication process. Micro-mechanical shocks are created by formation and disruption of these microscopic bubbles which in turn disrupt the structural and functional component of cells leading to cell lysis (CFSAN, 2). Ultrasound technology has been used to inactivate Gram-negative pathogens in perishable foods such as poultry and liquid food products. For instance, a 4 log 1 reduction of salmonellae was obtained in peptone water using 1 min treatment time; whereas, a 3 min ultrasonic treatment reduced only.8 log 1 of salmonellae in chocolate milk, which suggested something in chocolate milk enhanced resistance (Lee et al., 1989). 9

35 Ultraviolet light Ultraviolet (UV) light processing involves the use of radiation from the ultraviolet region of the electromagnetic spectrum. UV light is divided into four regions according to the range of wavelength: UV-A (315 to 4 nm), UV-B (28 to 315 nm), UV-C (2 to 28 nm), and the vacuum-uv range (1 to 2 nm) (Bolton 1999). Among the four regions UV-C light possesses the germicidal properties. The germicidal property of UV- C light is due to DNA mutations induced through absorption of UV light by DNA molecules (CFSAN, 2). This technology has been used for reducing the microbial loads on food surfaces. For instance, Sommers et al. (29) used UV-C irradiation at doses of 1, 2, and 4 J/cm 2 which resulted in 1.31, 1.49, and 1.93 log 1 CFU/g reductions of L. monocytogenes counts. UV light has poor penetration capacity. Since milk is an opaque liquid, UV-light does not penetrate it well; hence milk has to be presented to the system in a thin layer (Burton, 1951) Pulsed UV-light Pulsed UV-light sources provide a broad spectrum of radiation ranging from UVlight to infrared radiation (1 to 11 nm). This technology is gaining popularity as it inactivates microorganisms without any toxic by-products (FDA, 2). It can be used to inactivate microorganisms on the food surface and also can be used for in-package sterilization of food materials provided the packaging material is suitable for UV-light penetration (Butz and Tauscher, 22). Pulsed UV-light form thymine dimers which damages the DNA leading to the death of the microorganism. Krishnamurthy et al. (27) studied the inactivation of S. aureus in milk using flow-through pulsed UV-light. The study reported log 1 reduction varied from.55 to 7.26 when milk was treated at 5, 8, or 11 cm distance from UV-light strobe at 2, 3, or 4 ml/min flow rate. 1

36 2.4. High pressure processing Hite (1899) demonstrated that high pressure treatment could be used to preserve milk. Food preservation using high hydrostatic pressure is gaining popularity as an alternative to traditional heat-based methods which has detrimental effect on the quality of food (McClements et al., 21); whereas using HPP, the fresh qualities of food materials can be preserved Principles of high pressure processing of food There are two general principles for explaining how HPP affects food. The first is the Le Chatelier s Principle which states that, if a system at equilibrium experiences disturbances then the system tries to respond in a way that results in minimizing the disturbance (Pauling, 1964). This means high pressure favors the reactions that result in a decrease in volume but opposes the reactions that result in an increase in volume. The second is the Isostatic Principle, which states that pressure is transmitted instantly and uniformly throughout a liquid under pressure, i.e., independent of its size and geometry (Smelt, 1998) Description of HPP process In High Pressure Processing (HPP) of food, the food material is submerged in a pressure transmitting liquid which in turn is placed in a pressure vessel that can sustain the desired pressure. The pressure transmitting medium can be water, castor oil, silicon benzoate, ethanol, or glycol. Different factors are involved in selecting the pressure transmitting medium such as ability of the pressure transmitting fluid to protect the inner vessel surface from corrosion, the specific High Pressure (HP) system being used, the treatment temperature range and the viscosity of the food material under pressure (Hogan et al., 25). In general the high pressure processing of food involves four steps. Firstly, packing the food material in a sterilized container; secondly, loading the sample in the high pressure chamber; thirdly, giving the pressure treatment to the food sample; and fourthly, releasing the pressure and unloading the sample from the pressure vessel. Figure 2.1 shows the flow diagram of high pressure processing. 11

37 Package food in a sterilized container Load packaged food in a pressure chamber Fill the pressure chamber with water Heat the water to desired temperature Pressurize the chamber Hold under pressure Depressurize the chamber Remove processed food Figure 2.1 Flow diagram of high pressure processing (FAO, 21). Currently in the food industry, batch or semi-continuous HPP system are being used depending on the type of food material to be processed. Solid food products or products with large solid particles are treated by batch system only; whereas liquids and other pumpable products have the flexibility of receiving treatment by semi-continuous methods (Ting and Marshall, 22) Batch HPP process In batch systems, the product is placed in a high pressure chamber filled with the pressure-transmitting fluid. The product receives high pressure treatment by pressurizing the pressure-transmitting fluid either by a pump or by reducing the volume of the pressure chamber by a piston. Generally, water is used as the pressurizing medium, which can be compressed by up to 15 percent volume at pressure above 6 MPa. Once the 12

38 desired pressure is reached, the food material is held at the desired pressure for the required time period. During the hold time the valves are closed and pressure is maintained inside the chamber without any further energy input. After receiving the pressure treatment for the required time, the system is depressurized by releasing the pressure and the samples are taken out from the chamber. The system is then ready for next batch of fresh product (Ting and Marshall, 22). The time including the process of pressurization, holding and depressurization is referred to as the cycle time. The cycle time and the loading factor (i.e., the percentage of the vessel volume actually used for holding packaged product, primarily a factor of package shape) determine the throughput of the system (Hogan et al., 25) Semi-continuous HPP process Pumpable product has the advantage of being pumped into and out of the processing vessel through special high-transfer vessel valves and isolators (Ting and Marshall, 22). The semi-continuous systems that are currently being used in industry for liquids use a pressure vessel containing a free piston to compress liquid foods. The vessel is filled by a low pressure food pump, after displacing the free piston. The inlet port of the high pressure vessel is closed and the pressure transmitting medium is introduced behind the free piston to compress the food material after the vessel is filled. After holding the food product at the desired pressure for the required time, the pressure is released from the pressure transmitting medium. The treated food material is then collected in a sterile tank through a sterile discharge port (CFSAN, 2) Adiabatic heating during HPP Pressurization of compressible materials leads to a thermodynamically induced temperature change due to compressive work done against intermolecular forces. The temperature change is greater under adiabatic conditions. The adiabatic heating is also greater when the material is more compressible. As most food materials contain water they show similar compression heating characteristics as water, which is between 3 and 4ºC increase per 1 MPa, depending on the initial temperature of the food material. The 13

39 temperature increase during adiabatic heating depends on several factors such as process pressure, initial temperature, composition of food material, and pressure transmitting fluid (Rasanayagam et al., 23). During HPP, pressure acts instantaneously and uniformly throughout the food material irrespective of its shape, size, and composition. Hence a spatial pressure gradient within the pressure vessel generally is not taken into account. On the other hand there are at least two sources of spatial temperature gradients. The first one is due to the difference in compressibility of materials resulting in different temperature increase. The change in temperature during adiabatic compression and expansion is given below (Master et al., 24): dt dp T (2.1) C p where, T = Temperature P = Pressure α = Volumetric expansion coefficient ρ = Density C p = Specific heat The second source of temperature gradient is due to the transfer of heat between the pressure vessel contents, which includes food material, pressure transmitting fluid, and microorganisms, and the pressure and external environment. This heat transfer occurs as the metal pressure vessel is incompressible and does not increase its pressure during compression (de Heij et al., 23); whereas, the contents of the pressure vessel increase their temperatures due to adiabatic heating because of their compressible nature. As a result, product near the pressure vessel wall will be at a lower temperature than product in the center of the vessel (de Heij et al., 22; Ting and Marshall, 22). Hence during 14

40 HPP, different pressure-temperature-time profiles can be obtained at different locations in the pressure vessel resulting in non uniform distribution of microbial/enzyme inactivation, nutritional and/or sensorial quality degradation within the food product (Denys et al., 2) Critical process factors influencing effectiveness of HPP treatment on microorganism Type of microorganism The sensitivity of different microorganisms to pressure is very variable. In general, the more complex the microorganism is; the greater is the sensitivity to pressure treatment (Gould, 1995). In the case of bacteria, it is believed that Gram positive bacteria are more pressure resistant than the Gram negative bacteria which can be attributed to the difference in the cell wall structure of the two types of microorganisms. Presence of the thick peptidoglycan layer in Gram positive bacteria may result in higher pressure resistance than the Gram negative bacteria, which have a thinner peptidoglycan layer. Moreover, there exists a correlation between the morphology of the bacterial cell and the pressure sensitivity. Rod-shaped bacteria are the most sensitive; whereas, cocci tend to be more resistant (Cheftel, 1995; Ludwig and Schreck, 1997). Microbial cells at different growth phases vary in sensitivities to pressure treatment. For example, stationary phase cells are more resistant to pressure than exponentially growing cells (Pagan and Mackey, 2; Brul et al., 2; McClements et al., 21). The high barotolerance of stationary phase cells may be due to smaller size and spherical shape of the microorganisms. Also, accumulation of protein, carbohydrate in stationary phase cells may contribute to their barotolerance behavior (Isaacs et al., 1995). Wen et al. (29) showed that the log reductions of L. monocytogenes after high pressure treatment were significantly different (p <.1) for the different growth phase of the microorganism. For instance, when treated at 4 MPa for 18 sec the log reduction of L. monocytogenes decreased significantly (p <.1) from 5.1 in the late-log and stationary phase to 2.1 in the death phase to.3 in the long-term-survival phase. 15

41 Composition of food material The composition of food material in which the microorganisms are suspended can affect inactivation during and after pressure treatment (Cheftel, 1995). In some food materials the microorganisms are more resistant to pressure than the other. For example, milk induces more barotolerance in microorganisms than phosphate buffer (Patterson et al., 1995; Simpson and Gilmour, 1997; Garcia-Graells et al., 2). The fat present in milk has been associated with barotolerance of microorganisms (Garcia-Graells et al., 1999) Water activity (a w ) The change in value of water activity as a function of pressure has not yet been established. Reducing water activity from to resulted in substantial reduction of microorganisms in sucrose solution (Oxen and Knorr, 1993). Reducing the a w value seems to protect microorganism from HPP treatment, but on the other hand, recovery of the injured cells can also be inhibited by lowering the a w. Hence the net effect of a w on microorganisms is difficult to predict under pressure (Smelt, 1998). Hayman et al. (28) studied the effect of reduced a w on inactivation of L. monocytogenes during HPP. For instance, when cells were subjected to HPP in a medium with a w between.99 and.86, no survivors were detected, whereas, in medium with a w of.83 only 2.5 log 1 reduction was obtained. Moreover, when the a w of the medium was below.83 there was significant reduction (p <.1) in the effectiveness of HPP treatment. This study showed the baroprotective effect of reduced a w on inactivation of L. monocytogenes ph The ph of the food material may change as a function of treatment pressure. Decrease in ph by.5 units per 1 MPa increase in pressure in apple juice was reported by Heremans (1995). As ph decreases during HPP, the reduced ph makes the microorganism more susceptible to pressure and reduces the recovery of sublethally injured cells. Ionic bonds are also disrupted by reduced ph under pressure which in turn is responsible for denaturation of proteins (CFSAN, 2). 16

42 Treatment conditions The inactivation of microorganisms can be increased by increasing the magnitudes of treatment pressure, temperature, and/or processing time. Inactivation of microorganisms may not occur below a minimum critical pressure irrespective of the pressurization time (Hogan et al., 25). With increase in treatment pressure and treatment time, bacterial inactivation increases (Gervilla et al., 1997; Chen and Hoover, 23b, Chen and Hoover, 24 ). For example, for 6 min treatment time the approximate reduction values obtained for Y. enterocolitica at 3, 35, 4, and 45 MPa was 3, 4.5, 6, and 7 log 1 respectively in phosphate buffer (Chen and Hoover, 23b). Similarly, approximately 8 log 1 reductions of Y. enterocolitica was obtained at 3, 35, 4, and 45 MPa in approximately 45, 24, 11, and 8 min, respectively, in phosphate buffer (Chen and Hoover, 23b). Approxmately, 7.5 log 1 reductions of L. monocytogenes was obtained in UHT whole milk with treatment times of approximately 6, 165, 35, and 8.5 min at 3, 4, 5, and 6 MPa respectively (Chen and Hoover, 24). The mechanism of inactivation may differ depending on the level of pressure (Pagan and Mackey, 2). The rate of bacterial inactivation increased when the food temperature was increased above room temperature and to a lesser extent when the temperature was decreased below room temperature. Temperatures in the range of 45 to 5ºC seem to increase the inactivation rate of spoilage microorganisms and food pathogens (Chen and Hoover, 23a; Buzrul and Alpas, 25). For example, with a treatment time of 5 min at 5 MPa and 5ºC more than 8 log 1 reductions of L. monocytogenes was obtained, whereas at the same pressure level at 22ºC a treatment time of 35 min was needed to obtain the same level of log reductions (Chen and Hoover, 23a). Approxmately, 6 log 1 reductions of Alicyclobacillus acidoterrestris was obtained in BAM (bacillus acidocaldarius medium) broth with treatment times of approximately 16, 12, and 32 min at 35, 45, and 5ºC, respectively, with the treatment pressure being the same for all i.e., 35 MPa (Buzrul et al., 25). Temperatures in the range of 9 to 11ºC with pressures of 5-7 MPa were demonstrated to inactivate spore-forming bacteria such as C. botulinum (CFSAN, 2). 17

43 2.6. Mechanisms of inactivation Cell membrane and cell wall Researchers believe that the primary site of pressure damage is the cell membrane (Cheftel, 1995; Tholozan et al., 2; Ritz et al., 21). High pressure affects the cell permeability and ion exchange systems, which ultimately alters the carriers of transport system. Hence the homeostatic and barrier systems are disturbed. Tholozan et al. (2) reported complete efflux of intracellular potassium from stationary phase cells of L. monocytogenes Scott A and S. typhimurium when treated at 4 MPa. They also found that the membrane potential was decreased in both microorganisms when treated at 4 MPa or 6 MPa in citrate and phosphate buffer, respectively. High pressure also causes crystallization of membrane phospholipid, which can contribute to bacterial inactivation (Cheftel, 1995; Ritz et al., 21). Under high pressure, fluidity of the microbial cell membrane decreases. The decrease in membrane fluidity alters the functional properties of associated enzymes, which ultimately results in fragmentation of the phospholipids bilayer of cell membrane. Also, the enzyme and proteins associated with the membrane get denatured, resulting in death of microorganisms (Kato and Hayashi, 1999). Ritz et al., (21) investigated the bacterial membrane damage in L. monocytogens cells treated for 1 mins at 4 MPa in ph 5.6 citrate buffer. They compared the uptake of fluorescent stain (propidium iodide) by pressure treated cells and untreated cells. Pressure-treated cells were stained with the dye, which was only possible if the cell membranes were damaged, whereas no staining was observed in the untreated cells confirming damage to the cellular membrane Biochemical reactions According to Le Chatelier s priniciple, pressure enhances reactions which lead to a decrease in volume and that inhibits reactions which result in an increase in volume. Many researchers believe this rule works for protein precipitation under high pressure. High pressure treatment enhances protein-protein hydrophobic interactions and 18

44 hydrophobic reactions among protein molecules, which cause a decrease in volume. Hence these reactions are favored during HPP and lead to destruction of microbial cells. Several researchers also believe that microbial enzymes are key targets of pressure. Different phenomena such as membrane damage, protein denaturation and decrease in intracellular ph suggested that membrane bound enzymes related to efflux of protons might play an important role in pressure inactivation of microorganism. Movement of ions by membrane bound ATPase may also be altered because of inactivation of enzyme such as Na+ -K+ - ATPase and Ca +2 ATPase (Smelt, 1998) Genetic mechanism Nucleic acids are believed to be more resistant to pressure induced denaturation than proteins; however, researchers found that DNA and r-rna are ruptured by pressure in vivo (Chilton et al., 1997). Many studies also revealed that there was extreme condensation of DNA in high-pressure treated cells (Smelt, 1998). As enzymes are affected by high pressure, several phenomena controlled by enzymes such as DNA replication, transcription, and translation are also expected to be inhibited by high pressure (Cheftel, 1995). Several studies have been conducted on the effect of HPP on conformation of ribosomes using differential scanning calorimetry. Researchers attempted to correlate the ribosome-associated enthalpy (RAE) and cell viability. They compared the RAE of untreated cells with those of high pressure treated cells. They found there is decrease in value of RAE for pressure-treated cells, compared to untreated cells. Hence, they postulated that the decrease in RAE was due to dissociation or conformation of ribosomes under pressure. Accordingly, ribosomes were considered as a key factor for microbial inactivation under high pressure (Niven et al., 1999) Injury and recovery of microorganism A microorganism can be said to be injured when the microorganism survives a stress but at the same time it loses some of its distinctive qualities (Busta, 1976). According to Hartsell (1951), injured cells can be defined as those cells that can grow on 19

45 nonselective media, but not on the selective media after a given treatment. When a sublethal physical or chemical treatment is given to a population of microorganisms, the surviving population may have dead cells (lethally or irreversible injured), uninjured cells (normal cells), and injured cells (sublethally or reversibley injured), (Ray, 1979, 1989; Russel, 1984; McFeters, 1989; Bozoglu et al., 24). A schematic is given in Figure 2.1. Normal population of microbial cells Physical/Chemical stress Survivors Dead Injured Non-injured (Normal) Figure 2.2 Effect of sublethal treatments on microbial cells (Ray, 1979, 1989; Russel, 1984; McFeters, 1989; Bozoglu et al., 24). After receving a given sublethal stress, sublethally injured cells can repair the damage caused by the stress and can grow and divide under favorable conditions. This phenomenon is termed resuscitation as the cells are revived from apparent death conditions (Hurst, 1984). The injury and recovery of high pressure treated microorganisms has been studied (Kalchayanand et al., 1998; Hayman, 27; Bozoglu et al., 24; Bull et al., 25). For instance, Kalchayanand et al. (1998) studied the death and injury of four foodborne pathogens under hydrostatic pressure pasteurization. Their results showed that the loss of viability and injury of microorganisms at 25 C were very minimal upto 27 MPa and then increased at a rapid rate. At and above a pressure level of 276 MPa a large number 2

46 of survivors showed injury. When the treatment temperature was increased to 35 C and above, death and injury were increased Predictive microbiology for microorganisms Introduction and importance of predictive microbiology Predictive microbiology combines elements of microbiology, mathematics, and statistics to develop models that mathematically describe and predict the growth and death of microorganisms that have undergone some specific environmental conditions (Whiting, 1995). Predictive models are used by food industries to define critical control points (CCPs) in food processing operations, and in the subsequent development and implementation of hazard analysis and critical control points (HACCP) food safety systems. In addition, these models can be used by risk assessors to define management decisions that may reduce the risk of foodborne disease (ARS, 26) Primary, secondary and tertiary models Basically, the predictive models can be divided into three categories, i.e. primary, secondary, and tertiary models (Whiting and Buchanan, 1994). Primary models predict the change in the number of microorganisms with time, under a single given environmental condition. When the condition is favorable, the primary model will be a growth model; while under adverse conditions, the primary model will be an inactivation model. Some examples of growth and inactivation models are given in sections and 2.8.4, respectively. The secondary models describe the effect of environmental conditions such as physical, chemical, and biotic features, on the values of the parameters of a primary model. Two examples are given below. Arrhenius model The effect of a temperature change on the rate of microbial growth is given by the Arrhenius model as follows: 21

47 k E / RT Ae (2.2) where, k = growth rate (h -1 ) A = arrhenius constant (h -1 ) E a = activation energy of the reaction system (kj mol -1 ) R = universal gas constant (8.31 kj mol -1 K -1 ) T = absolute temperature (K) Davey (1989) modeled the growth rate by extending and modifying the Arrhenius equation, which describes k, as a function of temperature and water activity. The proposed relationship was: C1 C2 2 ln k C C 2 3 aw C4 aw (2.3) T T where, C, C 1, C 2, C 3, and C 4 are the model parameters. Square-root model Ratkowsky et al. (1982) suggested a simple empirical model between the growth rate and temperature: k b. ( T Tmin ) (2.4) where, b = constant T min = an estimated extrapolation of the regression line derived from the plot of temperature, T k vs 22

48 Many researchers have modified and extended the basic square root model to describe the growth rate of microorganisms in different environmental conditions. Ratkowsky et al. (1983) expanded equation (2.4) to include the entire biokinetic range of growth temperatures. k b ( T T ).(1 exp ( c( T T ))) (2.5). min max where, b, c = constants T = temperature T min = theoretical minimum temperature below which no growth is possible T max = theoretical maximum temperature beyond which growth is not possible McMeekin et al. (1987) extended equation (2.4), to model bacterial growth under combined effect of temperature and water activity. k b. ( T Tmin ). a w awmin (2.6) where, a w = water activity a w min = theoretical minimum water activity below which no growth is possible Tertiary level modeling applications assist in the use of primary and secondary level models by identifying and using mathematical expressions, software packages, and expert systems. At this level, the user needs to be aware of the equations in the underlying primary and secondary level models. A well known tertiary model is the Pathogen Modeling Program (PMP), which was developed by the USDA in 199s. PMP can describe the behavior of several pathogenic bacteria as a function of the environmental conditions. The model contains 23

49 growth curves, inactivation curves, cooling and irradiance models. It can also predict the time to turbidity or the time to toxin production of some pathogens. Seafood Spoilage and Safety Predictor (SSSP) is another well known program which emphasized on the spoilage of marine, fresh fish, as a function of temperature and gas atmosphere. SSSP can define a realistic time-temperature combination and can predict the microbial growth and the remaining shelf life of the material (Delieghere et al., 29) Growth models The growth of microorganisms is generally modeled by the sigmoidal curve. After a lethal treatment the survived microbial population has both injured and healthy cells. Some of the injured cells die and some of them take time to recover which is known as the lag phase. Similarly, the healthy cells may have a lag phase to overcome the effect of treatment. Once the injured and healthy cells pass the lag phase they start to grow like normal untreated cells although the growth rate may be slower than the untreated cells. Therefore, the recovery profile of the survived cells can also be described by a growth curve. So, it is important to know about growth models to understand the recovery of microorganism after processing. Generally, the sigmoidal growth curve is described by four parameters: the exponential growth rate (µ max ), the lag phase (λ), the initial inoculums level (N ), and the maximum cell density (N max ). The exponential growth rate is defined as the steepest tangent to the exponential phase, which is the tangent at the inflexion point, while the lag phase is defined as the time at which that extrapolated tangent line crosses the inoculums level (McMeekin et al., 1993). The generation time for a microorganism (GT) can be calculated if µ max is known by using the following equation: log 1 2 GT (2.7) max 24

50 The following growth models have been successfully used by the researchers to describe microbial growth Monod model The first growth model was developed by Monod (Devlieghere et al., 29). The model can give only one growth parameter, i.e., the growth rate (µ max ). It cannot model the lag phase and does not take into account the maximum cell density, although cells cannot grow indefinitely. kt Nt N e (2.8) where, N = population of microorganisms in CFU/ml at time zero N t = population of microorganisms in CFU/ml at time t k = ln 2/ GT t = time Gompertz model Gompertz function was introduced by Gibson et al. (1987) which helps to express the population of microbial cells as a function of time using the sigmoidal function. log N t A Dexp { exp[ B( t M)]} (2.9) where, N t = population of microorganisms in CFU/ml at time A = value of the lower asymptote (N ) D = difference in value between the upper and lower asymptotes (N max - N ) M = time at which the growth rate is maximum 25

51 B = relative growth rate at t=m Logistic model Gibson et al. (1987) proposed the Logistic model to predict microbial growth: log N t D A exp [ B( t M )] (2.1) The Logistic model gives similar result as Gompertz model, but it is a symmetrical model, while most of the growth curves are not. Hence Gompertz model is preferred over the Logistic model Baranyi Model Baranyi and Roberts (1994) proposed a dynamic growth model. In this model, the lag phase is attributed to the need to synthesize an unknown substrate q that is critical for growth. Once the cells have adjusted to the new environment, they grow exponentially until limited by restrictions dictated by the growth medium. where, x = population of cells at time t x max = maximum cell density dx q( t) x( t) m. max.[1 [ ] ] x( t) (2.11) dt q( t) 1 xmax q(t) = concentration of the limiting substrate, which changes with time and given by dq ( max. q ( t) ). dt m = characterizes the curvature before stationary phase. An explicit version of the Baranyi and Roberts model has also been derived, which is given below: 26

52 ln x( t) ln x m max A( t) 1 e 1 max A( t) ln[1 (2.12) m (ln x x ) m e max t 1 e q A( t) t ln[ 1 q ] (2.13) where, ν = the rate of increase of the limiting substrate, generally assumed to be equal to µ max x = Initial cell density q = initial concentration of the limiting substance Buchanan three- phase linear model Buchanan et al. (1997) proposed a three-phase growth model to describe the lag phase, exponential phase, and stationary growth phase of microorganisms. Lag phase: t tlag, Nt N Exponential phase: tlag t tmax, Nt N ( t tlag) Stationary phase: t t max, N t N max where, N t = population of microorganisms at time t in log CFU/ml N = initial population of microorganisms in log CFU/ml N max = maximum population of microorganisms supported by the environment in log CFU/ml t = elapsed time in h t lag = time when lag phase ends in h 27

53 t max = time when maximum population of microorganism is reached in h µ = specific growth rate in log CFU/ ml h According this model the lag phase is divided into two periods such as a) period to adapt to the new environment (t a ), b) period to generate energy for production of biological components needed for cell replication (t m ). Therefore, lag phase can be written as t lag t a t m The model assumes that maximum value of µ exists between the end of lag pahse and beginning of stationary phase and value of µ is zero both during lag and stationary phase Inactivation/survival model The ability to understand and model the inactivation/survival of pathogens in foods or during processing of food is critical to the safety of the food supply. In both thermal and nonthermal inactivation (such as high pressure, ultra violet light, and ultra sound), there are four commonly observed types of survival curves: linear, curves with shoulders, curves with tails, and curves of sigmoid shape. The linear curve is described by the first order model and the nonlinear curves (shoulder, tail, sigmoidal) are described by different nonlinear models such as Modified Gompertz, Log logistic, Weibull, and Fermi (Devlieghere et al., 29) First order model For decades, inactivation of microorganisms has been described by first order kinetics. In this approach it is assumed that all the cells in a population show equal resistance to a given treatment and the inactivation pattern is stochastic. This stochastic approach gives a linear relation between the logarithimic inactivation and treatment time at a constant treatment factor (pressure/temperature (Schaffner and Labuza, 1997). The first order model is given below, 28

54 log Nt N t k t (2.14) D 2.33 where, N = population of microorganisms in CFU/ml at time zero N t = population of microorganisms in CFU/ml at time t t = treatment time D = decimal reduction time (time required for one log reduction in the population of cells), which is obtained as the negative reciprocal of the slope of the semi logarithmic survivor curve k = rate of inactivation in time -1 (slope of the inactivation curve on a semi logarithmic plot). The value of k from different processing methods helps to compare the inactivation of a microorganism in a particular set of conditions; in other words, it compares the effectiveness of different processes with respect to inactivation of a microorganism. Another important kinetic parameter is z (for instance, change in temperature/pressure for which the decimal reduction time is changed by a factor of ten in heating/pressurizing process). Mathematically, it can be written as T2 T1 z T for a heating process D1 log D 2 P2 P1 and z p for pressurizing process (Mussa et al., 1999), where T 1 and T 2 ( C) are D1 log D 2 temperature corresponding to D 1 and D 2 (min) and, P 1 and P 2 (MPa) are pressure corresponding to D 1 and D 2 (min). 29

55 Log Logistic model The four-parameter Log Logistic equation proposed by Cole et al. (1993) to describe the thermal inactivation of microrganisms is given below: log N t (2.15) 4 ( log t) / ( ) 1 e where, α = upper asymptote in log CFU/ ml ω = lower asymptote in log CFU/ ml τ = position of maximum slope, i.e. the log time required to achieve the maximum rate of inactivation in log min σ = maximum slope, i.e., the maximum rate of inactivation in log CFU/ml/log min) As log t at t = is not defined, t = 1-6 was substituted at t =, Then from equation (2.15), the value of N can be calculated from the following equation: log N (2.16) 4 ( 6) / ( ) 1 e Now, the final equation can be written as follows: Nt log N 4 ( log t) / ( ) 4 ( 6) / ( ) 1 e 1 e (2.17) Modified Gompertz model The Gompertz equation as proposed by Gibson et al. (1987) was used as growth model at the beginning but later on it was also used for modeling the inactivation kinetics (Linton et al., 1995; Xiong, et al., 1999). The modified Gompertz model is given below: 3

56 N log N e BM e B ( t M ) t Ce Ce (2.18) where, M = time at which the absolute death rate is maximum B = relative death rate at M C = difference in the value of upper and lower asymptotes Weibull model The Weibull model takes population heterogeneity into account and assumes that each microorganism in a population behaves differently to a lethal agent. Hence the survival curve is the result of several inactivation curves, which gives rise to a nonlinear curve (van Boekel, 2). The Weibull model is given below: log N t N t ( ) (2.19) where, α = Scale parameter (time) β = Shape parameter (dimensionless) Depending on the shape of the inactivation curve, the value of β changes; when the inactivation curve is concave upward, β < 1, it indicates that the surviving cells are more resistant to the lethal agent (van Boekel, 2). Similarly when the curve is concave downward, β > 1, it indicates that the remaining cells are more susceptible to the lethal agent (van Boekel, 2). But when the survival curve is linear, β = 1, all cells have equal resistance. 31

57 Inactivation models under high pressure and/or temperature treatments Inactivation models for high pressure Most of the conventional food processing methods are known to compromise select food quality attributes, whereas the use of high pressure processing (HPP) technology is generally believed to retain most of the food qualities (Farr, 199). Hite (1899) demonstrated, at the turn of the 19th century, that high pressure can be used to destroy food microorganisms. However, commercial realization of the process has begun recently. Lack of data on high pressure destruction kinetics of food pathogens has raised some concerns among consumers regarding the safety of HPP products (Lechowich, 1993). Also, there is great concern about the pressure-resistance characteristics of some nonspore forming bacteria. L. monocytogenes is one of the nonspore forming, pathogenic, pressure resistant microorganisms. The success of high pressure treatment for processing of low acid foods depends on the availability of pressure destruction kinetic data on pathogenic microorganisms. Mussa et al. (1999) obtained kinetic data on high pressure destruction of L. monocytogenes and natural microflora (NM) based on first order kinetics, pressure destruction time (PDT) and Arrhenius type model in raw milk. They found two kinds of pressure destruction kinetics. The first was the instantaneous pressure kill (IPK) effect, where pressure was applied without any holding time. The second was first order destruction rate (k). Both IPK and k values increased with an increase in pressure, while the corresponding D-value decreased. Comparison of pressure sensitivity of L. monocytogenes and (NM) of milk showed that the IPK value of L. monocytogenes was greater than the IPK value of NM, indicating higher pressure sensitivity of L. monocytogenes to instantaneous pressure. In contrast, when the kinetics parameters (D, k, z p (where z p is the pressure range between which the decimal reduction time is changed by a factor of ten) were compared, L. monocytogenes was found to be more pressure resistant than NM. 32

58 Ritz et al. (2) provide a statistically significant (p <.5) linear model for the inactivation of L. monocytogenes relative to four variables of high pressure treatment (pressure, time, temperature, and ph of the treatment medium) and three of their interactions (pressure/temperature, ph/temperature, and ph/pressure). An optimal design of 4 runs was obtained using the Fedorov algorithm with SAS Software (Fedorov, 1972). Significance of factors and their interactions were studied by ANOVA. ANOVA analysis showed that all the four variables significantly (p <.1) affect the inactivation of the microorganisms by high pressure treatment. Dogan and Erkmen (24) studied high pressure inactivation kinetics of L. monocytogenes in broth, milk, peach juice, and orange juice. They found that L. monocytogenes was most resistant in milk. In their study Dogan and Erkmen, used a twostep linear regression approach on the linearized (logarithmic data) and calculated the kinetic parameters D, k, and z p. Chen and Hoover (24) studied the survival curves of L. monocytogenes Scott A inactivated by high hydrostatic pressure at seven pressure levels (3, 35, 4, 45, 5, 55, and 6 MPa) in UHT whole milk. Both concave and convex survival curves were obtained depending on pressure level. The Weibull model consistently produced a better fit than the Log-linear model. The two parameters of the Weibull model b (scale factor) and n (shape factor) were found to be pressure dependent. A linear relationship between b and pressure was found in a pressure range of 4 to 6 MPa; whereas a mean value of n was calculated within this pressure range, which was nearly constant. Hence, they proposed a simplified Weibull model that produced a fit comparable to the full model and provided reasonable predictions of survival data at pressures other than the experimental pressure levels within 4 to 6 MPa. Chen (27) investigated the survival curves of various foodborne pathogen including L. monocytogenes at 6 MPa and 21.5 C in UHT whole milk. The inactivation rates were rapid at the beginning of the treatment and became less as the treatment time was increased leading to the tailing of survival curves. For example, with.5 min treatment time, the reduction value of L. monocytogenes was 6.7 log 1, whereas 33

59 the log reduction value only increased by.9 when the treatment time was increased to 6 min. A linear model in addition two nonlinear models (Log-logistic and Weibull) were fitted to the data. The nonlinear models gave better fit than the linear model. Klotz et al. (27) gave a new modeling approach to the nonlinear inactivation kinetics of the pressure treated bacteria. The model still addresses a first order reaction kinetics but the inactivation rate changes inversely with the square root of time. The model also gave a mechanistic approach as the model parameters vary with pressure, i.e., the model was able to calculate the parameters analogous to D and z value with change in pressure. Buzrul et al. (28) studied the inactivation of Escherichia coli and Listeria innocua in whole milk at room temperature (~ 22 C) and at five pressure levels (4, 45, 5, 55 and 6 MPa). The inactivation curves were modeled first by the traditional two-parameter Weibull model. Also, the inactivation curves were fitted with one-parameter Weibull model, where the shape parameter (n) was taken as constant. The value of the shape constant was calculated by averaging the shape parameter at different pressure levels obtained from the two-parameter model. By modeling the inactivation curves with the one-parameter model the goodness of fit of the model was decreased slightly, but at the same time it reduced the complexity of the two-parameter model. In addition, the logarithm of the scale/time parameter was plotted against the high pressure, which was used to define a z p value analogous to the D value Inactivation models under high pressure and temperature treatment To increase microbial inactivation, pressure is combined with heat. This strategy is likely to be successful as there is evidence that microbial injury can occur at significantly lower pressures than are required for inactivation (Patterson et al., 1995). Patterson and Kilpatrick (1998) investigated the combined effects of high hydrostatic pressure and heat on inactivation of Escherichia coli O157:H7 NCTC 1279 and Staphylococcus aureus NCTC 1652 in poultry meat and ultra high temperature (UHT) milk. The combination of high pressure and temperature was found more 34

60 promising than either treatment alone. The inactivation curves of both microorganisms, in both the media were fitted with Gompertz equation. The Gompertz variables were expressed by a polynomial equation, which was used to develop a simple model for prediction of inactivation of both the pathogens at different combinations of pressure and temperature. Chen and Hoover (23a) studied the survival curves of L. monocytogenes Scott A inactivated by the combine effect of pressures (4 and 5 MPa) and temperatures (22, 4, 45 and 5 C) in UHT whole milk. Tailing was observed in all survival curves and the data were fitted to Log-logistic, Gompertz, and Weibull models. Weibull model had the best fit and provided prediction of inactivation L. monocytogenes at temperature other than the experimental temperature within the temperature range of 45-5 C. Buzrul and Alpas (24) investigated the survival curves of L. innocua CDW47 inactivated by the combine effect of pressures (138, 27, 276 and 345 MPa) and temperatures (25, 35, 45 and 5 C) in peptone solution. A linear and two nonlinear (Weibull and Log logistic) models were fitted to the data. Although linear models produced good fits for some pressure-temperature combinations, they were not as good as nonlinear models. Among the two nonlinear models, the Log logistic model produced better fit than the Weibull model. Buzrul et al. (25) studied survival curves of A. acidoterrestris at two pressure levels (35 and 45 MPa) and three temperature levels (35, 45 and 5 C) in BAM broth. The Weibull model was fitted to the data at various combinations of temperature and pressure, which resulted in upward concavity in the fitted curves indicating tailing. The study showed that the shape factor (n) of the Weibull model had different values for 35 MPa at 35, 45 and 5 C; whereas, the values were not different for 45 MPa at 45 and 5 C. For the scale factor (b) of the Weibull model, two linear empirical equations were obtained as a function of temperature for the two pressure levels. The values of b can be predicted at different temperature levels other than the experimental temperatures by the two empirical equations proposed by the authors. 35

61 Gao et al. (26) investigated the combined effects of high pressure and mild heat on the reduction of L. monocytogenes in milk buffer and a quadratic equation was developed using a response surface model (RSM). By analyzing response surface plots and their corresponding contour plots and by solving the quadratic equation, experimental values were shown to be in good agreement with predicted values Injury and recovery models Many models have been described earlier for modeling bacterial growth and inactivation. But very few attempts have been made to model the injury and recovery of microorganisms under various processing conditions. Hills and Mackey (1995) developed a two-compartment kinetic model, which can describe lethal/sublethal injury, resuscitation (i.e., recovery following sublethal injury), induced lag, and exponential growth of microorganism. The model was successfully used to model the injury and resuscitation data of Salmonella Typhimurium. Chawla et al. (1996) evaluated the repair and injury of heat-treated L. monocytogenes by using selective and nonselective plating media. The Gompertz equation was fitted to the data obtained from both selective and nonselective media using a SAS program, which performed nonlinear regression using the Gauss-Newton integration procedure. The Gompertz parameters were obtained to calculate the repair percentage as a function of time from which the repair time was calculated. A first order model described the repair trend of L. monocytogenes in broth closely. McKellar et al. (1997) studied the ability of L. monocytogenes Scott A to recover from heat injury and modeled recovery as a function of temperature and extent of cell injury. They modified the equation proposed by Hills and Mackey (1995) and used it for their modeling. Koseki et al. (27) studied the recovery of L. monocytogenes treated with high pressure (4, 45, 5, 55, 6 MPa) at room temperature (~25 ±.5 C) and stored at 1 C for 7 days. Two initial inoculum levels of 3 and 5 log 1 CFU/g were used for all the pressures levels and the population of L. monocytogenes was reduced below the detectable limit (1 CFU/g). It was observed that the bacterial count gradually increased during storage and reached 7-8 log 1 CFU/g after 7 days of storage, which was higher 36

62 than the initial inoculum levels (3 and 5 log 1 CFU/g). To measure the recovery, samples were examined at 14-day intervals from the treated ham and the treated samples were scored as either 1 or depending on the recovery or no recovery, respectively. A linear logistic regression model was fitted to the data set and the minimum HPP conditions required to keep the food safe during the storage period was estimated. The model was also able to predict the probability of L. monocytogenes recovery during the storage period following the HPP treatment. According to Corradini and Peleg (27) if the total population of survivors (colonies on nonselective media) and the total population of healthy (colonies on selective media) microorganisms after a lethal treatment can be modeled by Weibull model, then the difference between the two Weibull models can predict the population of injured cells at different times after the treatment Importance and need for present research Injury and recovery of microorganisms under high pressure Bacterial cells get injured lethally or sublethally when they are exposed to different physical and chemical treatments. The injured microorganisms are either permanently damaged (i.e., lethally injured; dead) or may recover (i.e., sublethally injured) in food materials or medium containing the necessary nutrients under conditions of optimum ph and temperature (Bozoglu et al., 24). Specific studies have shown that the injured and repaired cells showed variation in the metabolic processes depending on the nature of stress (Pagan and Mackey, 2; Pagan et al., 21; Wouters et al., 1998). The cell components damaged by sublethal stresses are the cell wall, cytoplasmic membrane, ribosomal RNA, and DNA, as well as some enzymes (Kalchayanand et al., 22; Niven et al., 1999; Ulmer et al., 2; Wouters et al., 1998). Injury has been determined as the differences in cell numbers, or +/- growth in the enrichment broth. But there have been no mechanistic studies published to date on injury of microrganisms (Hayman, 27). It is believed that injury is dependent largely on magnitude of pressure (Hayman, 21; Bozoglu et al., 24; Bull et al., 25). 37

63 Kalchayanand et al. (1998) studied the death (lethal) and injury (sublethal) of four foodborne pathogens under hydrostatic pressure pasteurization. Their result showed that the loss of viability and injury of microorganissms at 25 C were very minimal upto 27 MPa and then increased at a rapid rate. At and above a pressure level of 276 MPa a large population of survivors showed injury. When the treatment temperature was increased to 35 C and above, death and injury was increased. Bozoglu et al. (24) investigated repair of injured foodborne pressure-resistant pathogens such as L. monocytogenes, Escherichia coli, Salmonella Enteritidis and Staphylococcus aureus following HHP (High Hydrostatic Pressure) treatment of milk during storage at different temperatures. Three pressure levels such as 35, 45, and 55 MPa and three storage temperatures after processing such as 4, 22, and 3 C were considered. A shelf-life study of milk over a period of four weeks after pressure treatment at the above mentioned storage temperatures was carried out. The results were reported as +/- growth but not in CFUs. The shelf-life study was stopped when colony formation was detected on both selective and nonselective agar at two consecutive samplings. The three states of cells just after pressure treatment were defined as (i) active cells (AC), which can form visible colonies on both selective (MOX) and nonselective agar (TSYEA); (ii) primary injury (I1), which allowed microorganisms to form visible colonies on nonselective agar but not on selective agar, however colonies were formed on selective agar during prolonged storage; and (iii) secondary injury (I2), which did not allow microorganisms to form visible colonies either on nonselective or on selective agar, however colonies were first formed on nonselective agar and later on selective agar following prolonged storage. The study showed that immediately after processing the milk at 35 MPa there was growth on both MOX and TYSEA; following processing at 45 MPa injury (I1) was found and recovery was confirmed on MOX within one day at all storage temperatures; following processing at 55 MPa injury (I2) was found and recovery was confirmed on both the media with 6 days (at 4 C) or 1 day (22 and 3 C) of processing. 38

64 Bull et al. (25) investigated the recovery of injured L. monocytogenes in skim milk at three storage temperatures (4, 15, and 3 C) following a treatment of 45 MPa for 9 s. They reported that the maximum recovery of injured cells occurred after 24 to 72 h of storage at a storage temperature of 15 C Study of physiological changes in microorganisms caused by high pressure using DSC and other methods The exact mechanisms of inactivation of microorganisms by high pressure are still not known. High pressure is assumed to cause changes in the morphology, cell membrane, cell wall, biochemical reactions, and genetic mechanisms of microorganisms (Hoover et al., 1989; Welch et al., 1993; Cheftel 1995; Wouters et al., 1998). Figure 2.3 shows different parts of a prokaryotic cell. Figure 2.3 A schematic of a typical prokaryotic cell (Flu Wiki, 27, with permission) 39

65 Niven et al. (1999) studied the effect of hydrostatic pressure on ribosome conformation in E. coli by using differential scanning calorimetry. For the first time, this study showed the effect of high pressure on ribosomes of living cells. They found a correlation between loss of cell viability of pressure treated cells and a decrease in ribosome associated enthalpy (RAE). Therefore, they postulated that cell death and ribosome damage were related to each other. Tholozan et al. (2) examined the effect of high pressure on physiological characteristics of stationary phase L. monocytogenes and S. typhimurium in sodium citrate (ph 5.6) and sodium phosphate (ph 7.) following high pressure treatment at 15-6 MPa for 1 min. The changes in cell morphology of microbial cells were evident with the increase in pressure. Lysis of the cell membrane was observed with the highest pressure treatments and was inferred as the cause of cell death. The study also showed that the low intracellular potassium concentration of high pressure treated cells was not able to maintain an internal potassium concentration to allow the survival of cell. Extremely low ATP content of cells under medium and high pressure treatments resulted in uncoupling of the major cellular ATP- driven K + uptake system. Ritz et al. (21) examined effect of HPP (4 MPa for 1 min) on morphological and physiological features of L. monocytogenes in ph 5.6 citrate buffer. Scanning electron microscopy, light scattering by flow cytometry, and cell volume measurements were compared to evaluate and determine the morphological changes in cells after pressurization. The results from all these methods suggested that there was no change in the cellular morphology. The measurement of propidium iodide uptake followed by flow cytometry showed that a small part of the population preserved the membrane integrity. This study confirmed that by combining fluorescent dyes and flow cytometry, the physiological activities of the cells can be monitored and ultimately can be related to the viability of cells. Kaletunc et al. (24) studied the structural changes in Leuconostoc mesenteroides treated at 25 and 5 MPa pressures, 35 C temperature for 5 min. After treatment the microbial samples were examined by scanning electron microscope (SEM), 4

66 transmission electron microscope (TEM), and differential scanning calorimetry (DSC). The SEM micrographs demonstrated that with increase in pressure, dechaining and blister formation on the surface of cells also increased. TEM studies showed that components of the cell cytoplasm were affected by high-pressure treatment. DSC study showed that the denaturation of ribosomes increased with pressure. In the DSC thermograms, the reduction in the peak of intact ribosomes was related to the decrease in the population of viable cells. This study confirmed that inactivation of L. mesenteroides was due to the ribosomal denaturation. Hayman (27) studied the effects of high pressure on whole cells of L. monocytogenes in potassium phosphate buffer using micro-differntial scanning calorimetry (mdsc). This study showed that the thermograms of untreated cells had five peaks with the two largest transitions at temperatures 68.4 C and 87.8 C. These two peaks were related to cellular proteins and DNA, respectively. Cells treated at 2 MPa showed similar thermograms as untreated cells; whereas, cells treated at 4 MPa showed four peaks and cells treated at 6 MPa showed one peak. The peak for DNA was present in both the treatments; whereas, peak for proteins was absent in both of them. This study indicated that the proteins of whole cells were denatured by HPP and can be a mechanism of inactivation in vegetative Gram-positive bacteria Summary L. monocytogenes is a psychrotrophic microorganism that can grow in temperatures as low as at -.4 C. As L. monocytogenes is capable of growing at low temperature, it is a potential threat for food products kept at low temperature (i.e., refrigeration temperatures); milk and dairy products are two examples. Several outbreaks of listeriosis have been reported in the past two decades, which has been discussed in section Moreover, L. monocytogenes is relatively resistant to pressure in milk. Many growth models have been developed for L. monocytogenes under different environmental conditions. Similarly, several models have been developed for inactivation of L. monocytogenes under thermal and nonthermal processing conditions. Extensive 41

67 literature review showed that only Bozoglu et al. (24) and Bull et al. (25) have studied the injury and recovery of L. monocytogenes in high pressure treated milk. A few models were found that described the injury and recovery of L. monocytogenes under thermal processing conditions only. In addition, no model is currently available for describing the injury and recovery of microorganisms following high pressure or the combined effects of high pressure and temperature. High pressure acts on components of the cell such as cell membrane, DNA, and ribosome. Damage to one or more these components is believed to cause death of the cell. Similarly, recovery is believed to be due to the repair of these damaged cellular components. To assure the safety of milk during the storage period, it is important to develop a predictive model for recovery of injured L. monocytogenes by taking into account the extent of damage to cellular components under combined effect of temperature and pressure. 42

68 CHAPTER 3 GOALS, OBJECTIVES, AND HYPOTHESES L. monocytogenes is a psychrotrophic microorganism that can grow below the refrigeration temperature. Therefore, it is a potential threat for food products such as milk and dairy products that are kept at low temperatures. This microorganism can result in listeriosis, a foodborne illness that can be serious and fatal for susceptible individuals. Several outbreaks of listeriosis have been reported in the past two decades due to contamination of food products with L. monocytogenes. Many growth and inactivation models have been developed for L. monocytogenes under different environmental conditions. An extensive literature review shows that two studies have been previously performed on the injury and recovery of L. monocytogenes in high pressure treated milk. A few models were found describing the injury and recovery of L. monocytogenes under thermal processing conditions only, and no model is currently available for describing the injury and recovery of this microorganism under high pressure or the combined effects of high pressure and temperature. To assure the safety of milk during the storage period, it is important to develop a predictive model for injury and recovery of L. monocytogenes by taking into account the extent of damage to cellular components under the combined effects of temperature and pressure. Accordingly, the research question for this dissertation was: Can a predictive model for injury and recovery of L. monocytogenes following the combined high pressure and temperature treatment be developed? Consistent with the research question, the overall goal of the study was to evaluate the inactivation, injury, and recovery of L. monocytogenes and to develop a predictive model(s) for injury and recovery of L. monocytogenes following the high pressure and temperature treatment. The specific hypotheses and objectives leading to accomplishment of this goal are provided in the following section. 43

69 Hypothesis 1 Treatment temperature and pressure levels affect the inactivation of L. monocytogenes cells grown at 43 C. Hypothesis testing was done at a significance level of.5. Ho: mean log reductions at seven combinations of pressure and temperature (identified via subscripts 1, 2, 3,.., 7) are significantly different (µ 1 = µ 2 = µ 3 = µ 4 = µ 5 = µ 6 = µ 7 ) Ha: mean log reductions at seven combinations of pressure and temperature are not significantly different (µ 1 µ 2 µ 3 µ 4 µ 5 µ 6 µ 7 ) Objective 1 To evaluate and model the inactivation of L. monocytogenes in milk immediately after high pressure and temperature treatment. Hypothesis 2 Injury of L. monocytogenes cells immediately after high pressure and temperature treatment can be modeled. Objective 2 To evaluate and model the injury of L. monocytogenes in milk immediately after high pressure and temperature treatment. Hypothesis 3 Cellular protein is a potential target of high pressure and temperature. Objective 3 Study the extent of damage of cellular protein of L. monocytogenes and differentiate between protein and non protein injured/recovered cells under combined effect of temperature and pressure in milk medium. 44

70 Hypothesis 4 Study the extent of damage of cellular protein of L. monocytogenes and differentiate between protein and non protein injured/recovered cells under combined effect of temperature and pressure in milk medium. Objective 4 To develop, verify, and validate a predictive model for injury and recovery of L. monocytogenes following the high pressure and temperature. 45

71 CHAPTER 4 MATERIALS AND METHODS This chapter includes the experimental procedures followed to accomplish the research objectives. In addition, the description of statistical procedures for experimental data analysis, model fitting, model development, and model parameter determination are summarized 4.1. Overview The overall experimental plan is comprised of four phases. Each phase corresponds to a specific objective and the hypothesis to be tested for that objective. The four phases are as follows: Phase 1 focuses on evaluating and modeling the inactivation of L. monocytogenes in milk immediately after the combined effects of pressure and temperature. Phase 2 focuses on evaluating and modeling the injury of L. monocytogenes in milk immediately after the combined effects of pressure and temperature. Phase 3 focuses on studying calorimetric response of milk (uninoculated), untreated inoculated milk sample, and treated inoculated milk samples to determine differentiation, if any, in the thermogram peaks of these samples, i.e., for relating peak magnitudes and locations to the extent of damage of cellular proteins such as ribosomal protein. Phase 4 focuses on studying and modeling the time-dependent injury and recovery of L. monocytogenes in milk during storage following the high pressure and temperature treatment. For this, the milk samples were incubated at 4 C for 3 days. The samples were tested at different times to evaluate the injury and recovery of L. monocytogenes following the treatment. A predictive model, composed of submodels, was developed and validated for injury and recovery of L. monocytogenes following the high pressure and temperature treatment. 46

72 4.2. Phase -1 Generation of inactivation data immediately after combined high pressure and temperature treatment Preparation of inocula L. monocytogenes serotype 4b (ATCC 19115) was obtained from the Food Science Department of the Pennsylvania State University. Frozen cultures in 2% glycerol were kept in an ultra-low freezer (Sanyo Scientific Inc., Chatsworth, CA) at - 8 C. Stock cultures of L. monocytogenes were maintained on the tryptic soy agar slants supplemented with.6% yeast extract (TSAYE) (Difco, Sparks, MD) at 4 C. Every two weeks, these were transferred onto new slants. Confirmation tests for L. monocytogenes were done by API strip test (biomerieux, Inc., Durham, NC) and by plating the milk sample on Blood Agar Plate (Moltox, Inc., Boone, NC). L. monocytogenes cells were grown in 1 ml of tryptic soy broth supplemented with.6% yeast extract (TSBYE) (Difco) and incubated at 43 C for 14.5 to 15 h to obtain the stationary phase cells. Cells were harvested by centrifuging the broth at 3,3 g for 15 min at 4 C and were washed with 1 ml of.1% peptone water (Difco) after decanting the supernatant. Then the cells were resuspended in 1 ml of Ultra High Temperature (UHT) milk at 4 C after decanting the supernatant to obtain an initial inoculum of 1 7 ~1 8 CFU/ml. Milk samples were treated within 2 h after inoculation Preparation of milk samples for high pressure and temperature treatment Tips of transfer pipettes (Sigma-Aldrich, Milwaukee, WI) were cut from the top with an ethanol dipped scissors. Then, 5 ml of inoculated milk was transferred into the pipette using a sterile syringe and a needle. Pipette bulbs were sealed by pressing with an impulse heat sealer (MP-16, Impulse; Midwest Pacific, St. Louis, MO). Sealed pipette bulbs were placed in 11.6 mm x 6 mm stomacher bags (VWR, West Chester, PA) containing 1% chlorine and then heat sealed twice for safety (Hayman, 27). 47

73 High pressure and temperature treatment of the sample Description of high pressure unit Combined high pressure and temperature treatment of the milk samples was conducted using a pilot scale, 2-L HPP Unit (Avure Technologies, Kent, WA) with water as the pressure transmitting medium (Figure 4.1); where HPP denotes high pressure processing. The high pressure unit is comprised of the following major components: Water reservoir: This reservoir contains the pressurization medium. Distilled water was used as the pressurization medium. Pressure vessel: The pressure vessel is used for isostatic pressing of foods at elevated temperature. The vessel was internally pressurized with water through a high pressure tubing connection in the vessel sidewall. Heat was added to the vessel outer diameter during operation. Vessel internal dimensions were:.1 m inside diameter.25 m inside length with a nominal internal volume of approximately two liters. The vessel was designed for maximum pressure and temperature of 6 MPa and 9 C, respectively. Teflon basket: Present inside the pressure vessel, the Teflon basket was used to hold the packaged food material during processing. Oil jacket: A surrounding oil jacket was used to heat the pressure vessel. Intensifier: Intensifier is a high pressure pump, which was used to increase the pressure to desired levels. The power of pump is 5 hp. Heater: A 9 kw heater was used to heat the heating medium (oil). Thermocouples: Two K- type thermocouples were attached to the top cover of the vessel. One thermocouple measured the temperature of the food at the center and the other at the outer surface. Control panel and computer: The control panel was hard-wired to the HPP unit and controlled through the computer. The treatment conditions, i.e., the desired pressure, temperature, and time, were input through the computer. On the monitor, the reading of thermocouples could be displayed. 48

74 Figure 4.1. High pressure processing unit (Avure Technologies, Kent, WA) Experimental protocol for high pressure and temperature treatment of the samples This part of the experiment was conducted in the wet pilot plant of the Food Science Department of the Pennsylvania State University. In all the experiments, both pressure and temperature were used. First, the HPP unit was turned on and the desired treatment temperature was obtained, which was displayed on the computer monitor. Once the water in the pressure vessel reached the desired temperature, the pressure vessel was opened and milk samples were placed in the Teflon basket. Thereafter, required high pressure was applied for the duration of the treatment time. Temperature increase due to adiabatic heating was taken into consideration. The initial temperatures for all the experiments were predetermined so that the treatment temperatures were obtained after pressurization. For example, to treat the sample at 4 MPa and 27 C, the initial 49